Software Engineering Intern / Junior Developer

1w1 week ago

Newton Research

US · Internship · $70,000 – $110,000

About this role

Newton Research builds an AI-powered research and analysis platform used by enterprises to unlock insights from their data. Our platform connects to major data warehouses like BigQuery, Snowflake, Databricks, and Redshift. Interns work on production code from week one in a small, high-output team.

You'll build API endpoints with DRF serializers and viewsets serving data to React frontend, handling complex models with JSONFields and custom managers. Extend AI agent capabilities by adding tools to LangGraph-based agents and working on retrieval-augmented generation with vector embeddings. Write async task workers using RQ for document parsing and LLM inference pipelines.

Our stack includes Python 3.13, Django 5.2, PostgreSQL, Redis on backend; OpenAI, Anthropic APIs, LangChain for AI/ML; React 19, TypeScript on frontend; Docker, AWS, pytest with 4,700+ tests for infra. The codebase features 7,700+ lines of Django models and complex multi-table relationships. Testing uses pytest, Vitest, Playwright with parallel execution.

Improve test coverage with real database fixtures and mock external APIs. Ship frontend features with TanStack Query and SCSS Modules, including interactive charts. Debug AI output to fix hallucinations and irrelevant retrievals, building skills that separate AI-era developers.

Requirements

Solid Python fundamentals to write a class, debug a traceback, and reason about data structures
Familiarity with web APIs including HTTP methods, JSON serialization, request/response cycles
Comfort with Git for branching, rebasing, and resolving merge conflicts
Experience with at least one database including SQL queries and basic schema design
Genuine curiosity about AI/ML with use of LLM APIs, RAG pipeline, or model fine-tuning
Ability to debug AI-generated code
Django or Flask experience
React/TypeScript exposure even from a personal project

Responsibilities

Build API endpoints with DRF serializers and viewsets that serve data to React frontend
Extend AI agent capabilities by adding new tools to LangGraph-based agents
Work on retrieval-augmented generation with memory system using vector embeddings and semantic search
Write async task workers with RQ for document parsing and LLM inference pipelines
Improve test coverage using pytest with database fixtures and mock external APIs
Ship frontend features building React components with TypeScript and TanStack Query
Debug AI output to diagnose and fix agent hallucinations or irrelevant retrieval results